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Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference

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Chormai,  Pattarawat
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Technische Universität Berlin;
Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences;

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Fisher,  Simon E.
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Francks,  Clyde
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Imaging Genomics, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Radboud University Medical Center;

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Kong,  Xiangzhen
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Zhejiang University;
Zhejiang University School of Medicine;

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Citation

Chormai, P., Pu, Y., Hu, H., Fisher, S. E., Francks, C., & Kong, X. (2022). Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference. NeuroImage, 262: 119534. doi:10.1016/j.neuroimage.2022.119534.


Cite as: https://hdl.handle.net/21.11116/0000-000A-D2D7-6
Abstract
Lateralization is a fundamental characteristic of many behaviors and the organization of the brain, and atypical lateralization has been suggested to be linked to various brain-related disorders such as autism and schizophrenia. Right-handedness is one of the most prominent markers of human behavioural lateralization, yet its neurobiological basis remains to be determined. Here, we present a large-scale analysis of handedness, as measured by self-reported direction of hand preference, and its variability related to brain structural and functional organization in the UK Biobank (N = 36,024). A multivariate machine learning approach with multi-modalities of brain imaging data was adopted, to reveal how well brain imaging features could predict individual's handedness (i.e., right-handedness vs. non-right-handedness) and further identify the top brain signatures that contributed to the prediction. Overall, the results showed a good prediction performance, with an area under the receiver operating characteristic curve (AUROC) score of up to 0.72, driven largely by resting-state functional measures. Virtual lesion analysis and large-scale decoding analysis suggested that the brain networks with the highest importance in the prediction showed functional relevance to hand movement and several higher-level cognitive functions including language, arithmetic, and social interaction. Genetic analyses of contributions of common DNA polymorphisms to the imaging-derived handedness prediction score showed a significant heritability (h2=7.55%, p <0.001) that was similar to and slightly higher than that for the behavioural measure itself (h2=6.74%, p <0.001). The genetic correlation between the two was high (rg=0.71), suggesting that the imaging-derived score could be used as a surrogate in genetic studies where the behavioural measure is not available. This large-scale study using multimodal brain imaging and multivariate machine learning has shed new light on the neural correlates of human handedness.